Download or read book Methods in Brain Connectivity Inference through Multivariate Time Series Analysis written by Koichi Sameshima and published by CRC Press. This book was released on 2016-04-19 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interest in brain connectivity inference has become ubiquitous and is now increasingly adopted in experimental investigations of clinical, behavioral, and experimental neurosciences. Methods in Brain Connectivity Inference through Multivariate Time Series Analysis gathers the contributions of leading international authors who discuss different time

Download or read book Brain Informatics and Health written by Dominik Slezak and published by Springer. This book was released on 2014-07-14 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the International Conference on Brain Informatics and Health, BIH 2014, held in Warsaw, Poland, in August 2014, as part of 2014 Web Intelligence Congress, WIC 2014. The 29 full papers presented together with 23 special session papers were carefully reviewed and selected from 101 submissions. The papers are organized in topical sections on brain understanding; cognitive modelling; brain data analytics; health data analytics; brain informatics and data management; semantic aspects of biomedical analytics; healthcare technologies and systems; analysis of complex medical data; understanding of information processing in brain; neuroimaging data processing strategies; advanced methods of interactive data mining for personalized medicine.

Download or read book Cognitive Science Recent Advances and Recurring Problems written by Fred Adams and published by Vernon Press. This book was released on 2019-04-18 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book consists of an edited collection of original essays of the highest academic quality by seasoned experts in their fields of cognitive science. The essays are interdisciplinary, drawing from many of the fields known collectively as “the cognitive sciences.” Topics discussed represent a significant cross-section of the most current and interesting issues in cognitive science. Specific topics include matters regarding machine learning and cognitive architecture, the nature of cognitive content, the relationship of information to cognition, the role of language and communication in cognition, the nature of embodied cognition, selective topics in visual cognition, brain connectivity, computation and simulation, social and technological issues within the cognitive sciences, and significant issues in the history of neuroscience. This book will be of interest to both professional researchers and newer students and graduate students in the fields of cognitive science—including computer science, linguistics, philosophy, psychology and neuroscience. The essays are in English and are designed to be as free as possible of technical jargon and therefore accessible to young scholars and to scholars who are new to the cognitive neurosciences. In addition to several entries by single authors, the book contains several interesting roundtables where researchers contribute answers to a central question presented to those in the focus group on one of the core areas listed above. This exciting approach provides a variety of perspectives from across disciplines on topics of current concern in the cognitive sciences.

Download or read book Information based methods for neuroimaging analyzing structure function and dynamics written by Jesus M. Cortés and published by Frontiers Media SA. This book was released on 2015-05-07 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this Research Topic is to discuss the state of the art on the use of Information-based methods in the analysis of neuroimaging data. Information-based methods, typically built as extensions of the Shannon Entropy, are at the basis of model-free approaches which, being based on probability distributions rather than on specific expectations, can account for all possible non-linearities present in the data in a model-independent fashion. Mutual Information-like methods can also be applied on interacting dynamical variables described by time-series, thus addressing the uncertainty reduction (or information) in one variable by conditioning on another set of variables. In the last years, different Information-based methods have been shown to be flexible and powerful tools to analyze neuroimaging data, with a wide range of different methodologies, including formulations-based on bivariate vs multivariate representations, frequency vs time domains, etc. Apart from methodological issues, the information bit as a common unit represents a convenient way to open the road for comparison and integration between different measurements of neuroimaging data in three complementary contexts: Structural Connectivity, Dynamical (Functional and Effective) Connectivity, and Modelling of brain activity. Applications are ubiquitous, starting from resting state in healthy subjects to modulations of consciousness and other aspects of pathophysiology. Mutual Information-based methods have provided new insights about common-principles in brain organization, showing the existence of an active default network when the brain is at rest. It is not clear, however, how this default network is generated, the different modules are intra-interacting, or disappearing in the presence of stimulation. Some of these open-questions at the functional level might find their mechanisms on their structural correlates. A key question is the link between structure and function and the use of structural priors for the understanding of the functional connectivity measures. As effective connectivity is concerned, recently a common framework has been proposed for Transfer Entropy and Granger Causality, a well-established methodology originally based on autoregressive models. This framework can open the way to new theories and applications. This Research Topic brings together contributions from researchers from different backgrounds which are either developing new approaches, or applying existing methodologies to new data, and we hope it will set the basis for discussing the development and validation of new Information-based methodologies for the understanding of brain structure, function, and dynamics.

Download or read book ECG Time Series Variability Analysis written by Herbert F. Jelinek and published by CRC Press. This book was released on 2017-09-11 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: Divided roughly into two sections, this book provides a brief history of the development of ECG along with heart rate variability (HRV) algorithms and the engineering innovations over the last decade in this area. It reviews clinical research, presents an overview of the clinical field, and the importance of heart rate variability in diagnosis. The book then discusses the use of particular ECG and HRV algorithms in the context of clinical applications.

Download or read book World Congress on Medical Physics and Biomedical Engineering 2018 written by Lenka Lhotska and published by Springer. This book was released on 2018-05-29 with total page 815 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book (vol. 3) presents the proceedings of the IUPESM World Congress on Biomedical Engineering and Medical Physics, a triennially organized joint meeting of medical physicists, biomedical engineers and adjoining health care professionals. Besides the purely scientific and technological topics, the 2018 Congress will also focus on other aspects of professional involvement in health care, such as education and training, accreditation and certification, health technology assessment and patient safety. The IUPESM meeting is an important forum for medical physicists and biomedical engineers in medicine and healthcare learn and share knowledge, and discuss the latest research outcomes and technological advancements as well as new ideas in both medical physics and biomedical engineering field.

Download or read book Brain Neurotrauma written by Firas H. Kobeissy and published by CRC Press. This book was released on 2015-02-25 with total page 718 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the contribution from more than one hundred CNS neurotrauma experts, this book provides a comprehensive and up-to-date account on the latest developments in the area of neurotrauma including biomarker studies, experimental models, diagnostic methods, and neurotherapeutic intervention strategies in brain injury research. It discusses neurotrauma mechanisms, biomarker discovery, and neurocognitive and neurobehavioral deficits. Also included are medical interventions and recent neurotherapeutics used in the area of brain injury that have been translated to the area of rehabilitation research. In addition, a section is devoted to models of milder CNS injury, including sports injuries.

Download or read book Statistical Methods for High dimensional Data with Complex Correlation Structure Applied to the Brain Dynamic Functional Connectivity Study written by Maria Aleksandra Kudela and published by . This book was released on 2017 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: A popular non-invasive brain activity measurement method is based on the functional magnetic resonance imaging (fMRI). Such data are frequently used to study functional connectivity (FC) defined as statistical association among two or more anatomically distinct fMRI signals (Friston, 1994). FC has emerged in recent years as a valuable tool for providing a deeper understanding of neurodegenerative diseases and neuropsychiatric disorders, such as Alzheimer's disease and autism. Information about complex association structure in high-dimensional fMRI data is often discarded by a calculating an average across complex spatiotemporal processes without providing an uncertainty measure around it. First, we propose a non-parametric approach to estimate the uncertainty of dynamic FC (dFC) estimates. Our method is based on three components: an extension of a boot strapping method for multivariate time series, recently introduced by Jentsch and Politis (2015); sliding window correlation estimation; and kernel smoothing. Second, we propose a two-step approach to analyze and summarize dFC estimates from a task-based fMRI study of social-to-heavy alcohol drinkers during stimulation with avors. In the first step, we apply our method from the first paper to estimate dFC for each region subject combination. In the second step, we use semiparametric additive mixed models to account for complex correlation structure and model dFC on a population level following the study's experimental design. Third, we propose to utilize the estimated dFC to study the system's modularity defined as the mutually exclusive division of brain regions into blocks with intra-connectivity greater than the one obtained by chance. As a result, we obtain brain partition suggesting the existence of common functionally-based brain organization. The main contribution of our work stems from the combination of the methods from the fields of statistics, machine learning and network theory to provide statistical tools for studying brain connectivity from a holistic, multi-disciplinary perspective.

Download or read book Humanoid Robotics and Neuroscience written by Gordon Cheng and published by CRC Press. This book was released on 2014-12-19 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Humanoid robots are highly sophisticated machines equipped with human-like sensory and motor capabilities. Today we are on the verge of a new era of rapid transformations in both science and engineering-one that brings together technological advancements in a way that will accelerate both neuroscience and robotics. Humanoid Robotics and Neuroscienc

Download or read book The New Frontier of Network Physiology From Temporal Dynamics to the Synchronization and Principles of Integration in Networks of Physiological Systems written by Plamen Ch. Ivanov and published by Frontiers Media SA. This book was released on 2022-02-17 with total page 842 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning in Natural Complex Systems written by Andre Gruning and published by Frontiers Media SA. This book was released on 2023-04-11 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Fundamentals of Brain Network Analysis written by Alex Fornito and published by Academic Press. This book was released on 2016-03-04 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. Winner of the 2017 PROSE Award in Biomedicine & Neuroscience and the 2017 British Medical Association (BMA) Award in Neurology Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain

Download or read book Dynamic Connectivity Regression written by Ivor Cribben and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most statistical analyses of fMRI data assume that the nature, timing and duration of the psychological processes being studied are known. However, often it is hard to specify this information a priori. In this work we introduce a data-driven technique for partitioning the experimental time course into distinct temporal intervals with different multivariate functional connectivity patterns between a set of regions of interest (ROIs). The technique, called Dynamic Connectivity Regression (DCR), detects temporal change points in functional connectivity and estimates a graph, or set of relationships between ROIs, for data in the temporal partition that falls between pairs of change points. Hence, DCR allows for estimation of both the time of change in connectivity and the connectivity graph for each partition, without requiring prior knowledge of the nature of the experimental design. Permutation and bootstrapping methods are used to perform inference on the change points. The method is applied to various simulated data sets as well as to an fMRI data set from a study (N=26) of a state anxiety induction using a socially evaluative threat challenge. The results illustrate the method's ability to observe how the networks between different brain regions changed with subjects' emotional state.

Download or read book Time Series Modeling of Neuroscience Data written by Tohru Ozaki and published by CRC Press. This book was released on 2012-01-26 with total page 561 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods are required.Time Series Modeling of Neuroscience Data shows how to

Download or read book Statistical Parametric Mapping The Analysis of Functional Brain Images written by William D. Penny and published by Elsevier. This book was released on 2011-04-28 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, Statistical Parametric Mapping provides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis. An essential reference and companion for users of the SPM software Provides a complete description of the concepts and procedures entailed by the analysis of brain images Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data Stands as a compendium of all the advances in neuroimaging data analysis over the past decade Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes Structured treatment of data analysis issues that links different modalities and models Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible

Download or read book Statistical Modeling of Long Memory and Uncontrolled Effects in Neural Recordings written by Alexander Greaves-Tunnell and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scientific analyses of time series data are often formalized as statistical investigations targeting one or more aspects of a complex underlying dependence structure. In the multivariate time series setting, there are three main aspects of interest: dependence over time, between components of the multivariate observations, and across repeated trials of the experimental protocol. Classical methods for these data may not be equipped to account for issues such as long-range dependence or unexpected variation across experimental settings. On the other hand, it can be difficult to evaluate whether more recent methods, such as those that make use of deep neural networks, have made quantitatively verifiable progress towards alleviating these issues. There is thus an opportunity to develop tools that extend principled statistical perspectives to meet the demand of current practices and problems in applied time series analysis. Motivated by these considerations, this dissertation develops methodology for the identification, estimation, and prediction of scientifically relevant features in the dependence structure of multivariate time series data. While these contributions apply to a wide range of data-analytical settings, corresponding to the broad prevalence of time series data across the sciences, they are motivated in particular by the challenges raised in the analysis of brain activity data. Neuroscientists measure the locally aggregated activity of cortical neurons as electromagnetic waveforms, recording from multiple locations on the brain surface and across repeated recording trials for various subjects or conditions. The resulting data raise challenging, scientifically important questions that can be phrased in terms of the three aspects of time series dependence enumerated above. We first address the topic of dependence over time through the lens of long-range dependent multivariate time series. We develop a statistical criterion for long memory in deep recurrent neural networks, thus offering a principled alternative to the heuristic evaluations based on synthetic data. We show negative results that suggest even deep recurrent neural network models explicitly designed to capture long-range dependencies fail to do so in a standard language modeling setting. This motivates the development of a model for multivariate long memory time series data, which we define in the frequency domain and apply to the analysis of brain activity in different states of consciousness. Second, we develop a framework to model changes in the dependence structure, or functional connectivity, in recordings obtained from a repeated-stimulation experiment in the rhesus macaque cortex. The analysis flexibly captures nonlinear effects and incorporates information about the connectivity between brain regions prior to stimulation. It is further equipped to address questions of stability, informativeness, and similarity among the learned features. The method improves the prediction accuracy of stimulation-induced connectivity change and provides new insights on the factors mediating this response. Finally, we continue our analysis of the brain-stimulation data to reveal previously unreported variation both between subjects and across experimental trials. We show that extensions to a simple regression model, either accounting for a more complex variance structure or estimating unmodeled confounders, can successfully mitigate the dramatic loss in predictive accuracy that results from applying the model to predict the results of previously unobserved experimental trials. Together, the contributions of this dissertation develop the capacity to detect, estimate, and predict scientifically meaningful aspects of the dependence structure in multivariate time series data.

Download or read book Fundamentals of Brain Network Analysis written by Alex Fornito and published by Academic Press. This book was released on 2016-03-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization.